{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dac5bcb4-e08d-4b94-b42a-b0e54ba46471",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: ipynbname==2023.2.0.0 in /home/node/.local/lib/python3.8/site-packages (from -r ../pycjs/requirements.txt (line 1)) (2023.2.0.0)\n",
      "Requirement already satisfied: ipython==8.0.1 in /usr/local/lib/python3.8/dist-packages (from -r ../pycjs/requirements.txt (line 2)) (8.0.1)\n",
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      "Requirement already satisfied: matplotlib>=3.5.3 in /home/node/.local/lib/python3.8/site-packages (from -r ../pycjs/requirements.txt (line 5)) (3.7.4)\n",
      "Requirement already satisfied: node-vm2==0.4.7 in /home/node/.local/lib/python3.8/site-packages (from -r ../pycjs/requirements.txt (line 6)) (0.4.7)\n",
      "Requirement already satisfied: numpy>=1.21.5 in /home/node/.local/lib/python3.8/site-packages (from -r ../pycjs/requirements.txt (line 7)) (1.24.4)\n",
      "Requirement already satisfied: pandas>=1.4.4 in /home/node/.local/lib/python3.8/site-packages (from -r ../pycjs/requirements.txt (line 8)) (2.0.3)\n",
      "Requirement already satisfied: ipykernel in /usr/local/lib/python3.8/dist-packages (from ipynbname==2023.2.0.0->-r ../pycjs/requirements.txt (line 1)) (6.7.0)\n",
      "Requirement already satisfied: pickleshare in /usr/local/lib/python3.8/dist-packages (from ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (0.7.5)\n",
      "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.8/dist-packages (from ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (4.8.0)\n",
      "Requirement already satisfied: pygments in /usr/local/lib/python3.8/dist-packages (from ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (2.11.2)\n",
      "Requirement already satisfied: black in /usr/local/lib/python3.8/dist-packages (from ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (21.12b0)\n",
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      "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.8/dist-packages (from ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (60.5.0)\n",
      "Requirement already satisfied: traitlets>=5 in /usr/local/lib/python3.8/dist-packages (from ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (5.1.1)\n",
      "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (3.0.24)\n",
      "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.8/dist-packages (from ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (0.1.3)\n",
      "Requirement already satisfied: stack-data in /usr/local/lib/python3.8/dist-packages (from ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (0.1.4)\n",
      "Requirement already satisfied: jedi>=0.16 in /usr/local/lib/python3.8/dist-packages (from ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (0.18.1)\n",
      "Requirement already satisfied: decorator in /usr/local/lib/python3.8/dist-packages (from ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (5.1.1)\n",
      "Requirement already satisfied: tornado>=6.1.0 in /usr/local/lib/python3.8/dist-packages (from jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (6.1)\n",
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      "Requirement already satisfied: jupyter-server~=1.4 in /usr/local/lib/python3.8/dist-packages (from jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (1.13.3)\n",
      "Requirement already satisfied: nbclassic~=0.2 in /usr/local/lib/python3.8/dist-packages (from jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (0.3.5)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (21.3)\n",
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      "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.8/dist-packages (from jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (4.9.1)\n",
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      "Requirement already satisfied: importlib-resources>=3.2.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.5.3->-r ../pycjs/requirements.txt (line 5)) (5.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.5.3->-r ../pycjs/requirements.txt (line 5)) (3.0.6)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.5.3->-r ../pycjs/requirements.txt (line 5)) (2.8.2)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/node/.local/lib/python3.8/site-packages (from matplotlib>=3.5.3->-r ../pycjs/requirements.txt (line 5)) (4.47.2)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/node/.local/lib/python3.8/site-packages (from matplotlib>=3.5.3->-r ../pycjs/requirements.txt (line 5)) (0.12.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/node/.local/lib/python3.8/site-packages (from matplotlib>=3.5.3->-r ../pycjs/requirements.txt (line 5)) (1.1.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/node/.local/lib/python3.8/site-packages (from matplotlib>=3.5.3->-r ../pycjs/requirements.txt (line 5)) (1.4.5)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /home/node/.local/lib/python3.8/site-packages (from pandas>=1.4.4->-r ../pycjs/requirements.txt (line 8)) (2023.4)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.8/dist-packages (from pandas>=1.4.4->-r ../pycjs/requirements.txt (line 8)) (2021.3)\n",
      "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.8/dist-packages (from importlib-resources>=3.2.0->matplotlib>=3.5.3->-r ../pycjs/requirements.txt (line 5)) (3.7.0)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.8/dist-packages (from jedi>=0.16->ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (0.8.3)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.8/dist-packages (from jinja2>=2.1->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (2.0.1)\n",
      "Requirement already satisfied: nbconvert in /usr/local/lib/python3.8/dist-packages (from jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (6.4.0)\n",
      "Requirement already satisfied: prometheus-client in /usr/local/lib/python3.8/dist-packages (from jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (0.12.0)\n",
      "Requirement already satisfied: anyio<4,>=3.1.0 in /usr/local/lib/python3.8/dist-packages (from jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (3.5.0)\n",
      "Requirement already satisfied: nbformat in /usr/local/lib/python3.8/dist-packages (from jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (5.1.3)\n",
      "Requirement already satisfied: terminado>=0.8.3 in /usr/local/lib/python3.8/dist-packages (from jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (0.12.1)\n",
      "Requirement already satisfied: websocket-client in /usr/local/lib/python3.8/dist-packages (from jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (1.2.3)\n",
      "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.8/dist-packages (from jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (1.8.0)\n",
      "Requirement already satisfied: jupyter-client>=6.1.1 in /usr/local/lib/python3.8/dist-packages (from jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (7.1.1)\n",
      "Requirement already satisfied: argon2-cffi in /usr/local/lib/python3.8/dist-packages (from jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (21.3.0)\n",
      "Requirement already satisfied: pyzmq>=17 in /usr/local/lib/python3.8/dist-packages (from jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (22.3.0)\n",
      "Requirement already satisfied: jsonschema>=3.0.1 in /usr/local/lib/python3.8/dist-packages (from jupyterlab-server~=2.3->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (4.4.0)\n",
      "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.8/dist-packages (from jupyterlab-server~=2.3->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (0.3)\n",
      "Requirement already satisfied: babel in /usr/local/lib/python3.8/dist-packages (from jupyterlab-server~=2.3->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (2.9.1)\n",
      "Requirement already satisfied: json5 in /usr/local/lib/python3.8/dist-packages (from jupyterlab-server~=2.3->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (0.9.6)\n",
      "Requirement already satisfied: requests in /usr/lib/python3/dist-packages (from jupyterlab-server~=2.3->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (2.22.0)\n",
      "Requirement already satisfied: notebook<7 in /usr/local/lib/python3.8/dist-packages (from nbclassic~=0.2->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (6.4.7)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.8/dist-packages (from pexpect>4.3->ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (0.7.0)\n",
      "Requirement already satisfied: wcwidth in /usr/local/lib/python3.8/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (0.2.5)\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib>=3.5.3->-r ../pycjs/requirements.txt (line 5)) (1.14.0)\n",
      "Requirement already satisfied: typing-extensions>=3.10.0.0 in /usr/local/lib/python3.8/dist-packages (from black->ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (4.0.1)\n",
      "Requirement already satisfied: tomli<2.0.0,>=0.2.6 in /usr/local/lib/python3.8/dist-packages (from black->ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (1.2.3)\n",
      "Requirement already satisfied: mypy-extensions>=0.4.3 in /usr/local/lib/python3.8/dist-packages (from black->ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (0.4.3)\n",
      "Requirement already satisfied: pathspec<1,>=0.9.0 in /usr/local/lib/python3.8/dist-packages (from black->ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (0.9.0)\n",
      "Requirement already satisfied: click>=7.1.2 in /usr/local/lib/python3.8/dist-packages (from black->ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (8.0.3)\n",
      "Requirement already satisfied: platformdirs>=2 in /usr/local/lib/python3.8/dist-packages (from black->ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (2.4.1)\n",
      "Requirement already satisfied: debugpy<2.0,>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from ipykernel->ipynbname==2023.2.0.0->-r ../pycjs/requirements.txt (line 1)) (1.5.1)\n",
      "Requirement already satisfied: nest-asyncio in /usr/local/lib/python3.8/dist-packages (from ipykernel->ipynbname==2023.2.0.0->-r ../pycjs/requirements.txt (line 1)) (1.5.4)\n",
      "Requirement already satisfied: asttokens in /usr/local/lib/python3.8/dist-packages (from stack-data->ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (2.0.5)\n",
      "Requirement already satisfied: executing in /usr/local/lib/python3.8/dist-packages (from stack-data->ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (0.8.2)\n",
      "Requirement already satisfied: pure-eval in /usr/local/lib/python3.8/dist-packages (from stack-data->ipython==8.0.1->-r ../pycjs/requirements.txt (line 2)) (0.2.1)\n",
      "Requirement already satisfied: idna>=2.8 in /usr/lib/python3/dist-packages (from anyio<4,>=3.1.0->jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (2.8)\n",
      "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.8/dist-packages (from anyio<4,>=3.1.0->jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (1.2.0)\n",
      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=3.0.1->jupyterlab-server~=2.3->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (0.18.1)\n",
      "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=3.0.1->jupyterlab-server~=2.3->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (21.4.0)\n",
      "Requirement already satisfied: argon2-cffi-bindings in /usr/local/lib/python3.8/dist-packages (from argon2-cffi->jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (21.2.0)\n",
      "Requirement already satisfied: defusedxml in /usr/local/lib/python3.8/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (0.7.1)\n",
      "Requirement already satisfied: testpath in /usr/local/lib/python3.8/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (0.5.0)\n",
      "Requirement already satisfied: nbclient<0.6.0,>=0.5.0 in /usr/local/lib/python3.8/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (0.5.10)\n",
      "Requirement already satisfied: bleach in /usr/local/lib/python3.8/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (4.1.0)\n",
      "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.8/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (1.5.0)\n",
      "Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.8/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (0.1.2)\n",
      "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.8/dist-packages (from nbconvert->jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (0.8.4)\n",
      "Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from argon2-cffi-bindings->argon2-cffi->jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (1.15.0)\n",
      "Requirement already satisfied: webencodings in /usr/local/lib/python3.8/dist-packages (from bleach->nbconvert->jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (0.5.1)\n",
      "Requirement already satisfied: pycparser in /usr/local/lib/python3.8/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->jupyter-server~=1.4->jupyterlab>=3.2.8->-r ../pycjs/requirements.txt (line 4)) (2.21)\n",
      "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 23.3.2 is available.\n",
      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install -r ../pycjs/requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ece3ae23-36fa-403f-99e6-cd43d78363bd",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- Add root directory \"/home/node/notebook\" to system path.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "cur_dir = os.getcwd()\n",
    "pkg_rootdir = os.path.dirname(cur_dir)  # *注意改为项目自定义包的根路径\n",
    "# print(pkg_rootdir)\n",
    "if pkg_rootdir not in sys.path:  # 解决ipynb引用上层路径中的模块时的ModuleNotFoundError问题\n",
    "    sys.path.append(pkg_rootdir)\n",
    "    print('-- Add root directory \"{}\" to system path.'.format(pkg_rootdir))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8a57dcc-5f4a-44bb-9e79-98791141fb0e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/node/.local/lib/python3.8/site-packages/node_vm2/__init__.py:17: FutureWarning: node_vm2 is deprecated. Please use deno_vm instead.\n",
      "  warnings.warn(\"node_vm2 is deprecated. Please use deno_vm instead.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import copy\n",
    "import pandas as pd\n",
    "\n",
    "from node_vm2 import NodeVM\n",
    "\n",
    "from pycjs.open_digger import vm_option_open_digger, get_export_module_open_digger\n",
    "from pycjs.vm_context_manager import VMContext\n",
    "from pycjs.metrics import MultiMime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d8b412ba-6d99-4a22-adca-601ef83a7476",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "year = 2023\n",
    "startYear = 2019\n",
    "endYear = year\n",
    "startMonth = 1\n",
    "endMonth = 12\n",
    "years = [i + startYear for i in range(5)]\n",
    "baseOptions = {\n",
    "   \"startYear\": startYear,\n",
    "   \"endYear\": endYear,\n",
    "   \"startMonth\": startMonth,\n",
    "   \"endMonth\": endMonth,\n",
    "   \"groupTimeRange\": 'year',\n",
    "   \"order\": 'DESC'\n",
    "}\n",
    "\n",
    "localOptions = {\n",
    "   \"limit\": 10\n",
    "}\n",
    "\n",
    "options = copy.deepcopy(baseOptions)\n",
    "options.update(localOptions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "83e519f1-4b04-46dd-93bd-5c688c5159a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>platform</th>\n",
       "      <th>name</th>\n",
       "      <th>openrank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4542716</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>NixOS/nixpkgs</td>\n",
       "      <td>[10178.42, 12807.93, 16484.33, 18068.7, 22368.27]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12888993</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>home-assistant/core</td>\n",
       "      <td>[7694.14, 10329.19, 11668.2, 14196.43, 15681.98]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41881900</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>microsoft/vscode</td>\n",
       "      <td>[11551.64, 12556.42, 12051.88, 12239.15, 13271...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72685026</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>MicrosoftDocs/azure-docs</td>\n",
       "      <td>[15179.16, 15635.02, 12386.24, 11419.23, 10400...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65600975</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>pytorch/pytorch</td>\n",
       "      <td>[7157.69, 8648.41, 8850.87, 8058.33, 10182.45]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15634981</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>godotengine/godot</td>\n",
       "      <td>[5848.35, 5919.27, 6159.23, 7556.08, 9906.06]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31792824</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>flutter/flutter</td>\n",
       "      <td>[11750.07, 15945.87, 13022, 10033.56, 9367.81]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19745004</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>odoo/odoo</td>\n",
       "      <td>[5106.76, 5544.93, 5518.5, 7004.32, 9325.34]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>632909818</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>elidianaandrade/dio-lab-open-source</td>\n",
       "      <td>[0, 0, 0, 0, 8447.2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>197275551</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>microsoft/winget-pkgs</td>\n",
       "      <td>[0, 1409.37, 3971.84, 5729.5, 8269.27]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id platform                                 name  \\\n",
       "0    4542716   GitHub                        NixOS/nixpkgs   \n",
       "1   12888993   GitHub                  home-assistant/core   \n",
       "2   41881900   GitHub                     microsoft/vscode   \n",
       "3   72685026   GitHub             MicrosoftDocs/azure-docs   \n",
       "4   65600975   GitHub                      pytorch/pytorch   \n",
       "5   15634981   GitHub                    godotengine/godot   \n",
       "6   31792824   GitHub                      flutter/flutter   \n",
       "7   19745004   GitHub                            odoo/odoo   \n",
       "8  632909818   GitHub  elidianaandrade/dio-lab-open-source   \n",
       "9  197275551   GitHub                microsoft/winget-pkgs   \n",
       "\n",
       "                                            openrank  \n",
       "0  [10178.42, 12807.93, 16484.33, 18068.7, 22368.27]  \n",
       "1   [7694.14, 10329.19, 11668.2, 14196.43, 15681.98]  \n",
       "2  [11551.64, 12556.42, 12051.88, 12239.15, 13271...  \n",
       "3  [15179.16, 15635.02, 12386.24, 11419.23, 10400...  \n",
       "4     [7157.69, 8648.41, 8850.87, 8058.33, 10182.45]  \n",
       "5      [5848.35, 5919.27, 6159.23, 7556.08, 9906.06]  \n",
       "6     [11750.07, 15945.87, 13022, 10033.56, 9367.81]  \n",
       "7       [5106.76, 5544.93, 5518.5, 7004.32, 9325.34]  \n",
       "8                               [0, 0, 0, 0, 8447.2]  \n",
       "9             [0, 1409.37, 3971.84, 5729.5, 8269.27]  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use context manager 'with'\n",
    "with NodeVM(**vm_option_open_digger) as vm:\n",
    "    # set show_indexes=True to display functions in the export module.\n",
    "    export_module = get_export_module_open_digger(vm, show_indexes=False)\n",
    "    openDigger = export_module.openDigger\n",
    "    data_repo_openrank = openDigger.index.openrank.getRepoOpenrank(options)\n",
    "    df_data_repo_openrank = pd.DataFrame(list(data_repo_openrank))\n",
    "\n",
    "df_data_repo_openrank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d3c21d59-54b8-4248-9b66-13aa68832832",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                              export_indexes\n",
      "0                              openDigger.label.getLabelData\n",
      "1                           openDigger.label.getPlatformData\n",
      "2                                   openDigger.render.plotly\n",
      "3                          openDigger.driver.neo4j.getClient\n",
      "4                              openDigger.driver.neo4j.query\n",
      "5                        openDigger.driver.neo4j.queryStream\n",
      "6                        openDigger.driver.neo4j.parseRecord\n",
      "7                  openDigger.driver.clickhouse.getNewClient\n",
      "8                         openDigger.driver.clickhouse.query\n",
      "9                   openDigger.driver.clickhouse.queryStream\n",
      "10                openDigger.driver.clickhouse.insertRecords\n",
      "11                 openDigger.index.activity.getRepoActivity\n",
      "12                 openDigger.index.activity.getUserActivity\n",
      "13       openDigger.index.activity.getRepoActivityWithDetail\n",
      "14       openDigger.index.activity.getUserActivityWithDetail\n",
      "15                 openDigger.index.openrank.getRepoOpenrank\n",
      "16                 openDigger.index.openrank.getUserOpenrank\n",
      "17        openDigger.index.openrank.getRepoCommunityOpenrank\n",
      "18                   openDigger.index.attention.getAttention\n",
      "19                openDigger.metric.chaoss.codeChangeCommits\n",
      "20                        openDigger.metric.chaoss.issuesNew\n",
      "21     openDigger.metric.chaoss.issuesAndChangeRequestActive\n",
      "22                     openDigger.metric.chaoss.issuesClosed\n",
      "23                        openDigger.metric.chaoss.busFactor\n",
      "24           openDigger.metric.chaoss.changeRequestsAccepted\n",
      "25           openDigger.metric.chaoss.changeRequestsDeclined\n",
      "26          openDigger.metric.chaoss.issueResolutionDuration\n",
      "27  openDigger.metric.chaoss.changeRequestResolutionDuration\n",
      "28                  openDigger.metric.chaoss.codeChangeLines\n",
      "29                  openDigger.metric.chaoss.newContributors\n",
      "30           openDigger.metric.chaoss.changeRequestsDuration\n",
      "31                openDigger.metric.chaoss.issueResponseTime\n",
      "32        openDigger.metric.chaoss.changeRequestResponseTime\n",
      "33                    openDigger.metric.chaoss.technicalFork\n",
      "34    openDigger.metric.chaoss.changeRequestsAcceptanceRatio\n",
      "35          openDigger.metric.chaoss.repoActiveDatesAndTimes\n",
      "36          openDigger.metric.chaoss.userActiveDatesAndTimes\n",
      "37                         openDigger.metric.chaoss.issueAge\n",
      "38                 openDigger.metric.chaoss.changeRequestAge\n",
      "39             openDigger.metric.chaoss.inactiveContributors\n",
      "40                     openDigger.metric.chaoss.contributors\n",
      "41                          openDigger.metric.xlab.repoStars\n",
      "42                   openDigger.metric.xlab.repoParticipants\n",
      "43             openDigger.metric.xlab.userEquivalentTimeZone\n",
      "44           openDigger.metric.xlab.contributorEmailSuffixes\n",
      "45                                        openDigger.getRank\n"
     ]
    }
   ],
   "source": [
    "with NodeVM(**vm_option_open_digger) as vm:\n",
    "    # set show_indexes=True to display functions in the export module.\n",
    "    df_options = {\n",
    "        \"max_colwidth\": 60,\n",
    "        # \"rows\": range(40),\n",
    "        \"columns\": [\"export_indexes\"],\n",
    "        # \"apply_func\": {\"func\": lambda x: sum([len(x_elem) for x_elem in x]) < 25, \"axis\": 1}\n",
    "        }\n",
    "    get_export_module_open_digger(vm, show_indexes=True, df_options=df_options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b8a39074-d898-451f-a77d-d8cf6509521c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use vm_context_manager as a manual context manager\n",
    "vmc = VMContext(**vm_option_open_digger)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c33a9d11-1084-48d4-aa0a-a0074b0ea542",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<node_vm2.NodeVM at 0x7f5cf7c03430>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vmc.open()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4479b888-109e-4227-b555-95935782b15d",
   "metadata": {},
   "outputs": [],
   "source": [
    "vm = vmc.vm\n",
    "export_module = get_export_module_open_digger(vm)\n",
    "openDigger = export_module.openDigger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6e290554-7740-4fe5-8c92-499d1103a8cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>platform</th>\n",
       "      <th>name</th>\n",
       "      <th>openrank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1580956</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>chenrui333</td>\n",
       "      <td>[674.78, 1261.4, 1533.26, 988, 2039.17]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>663432</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>bdraco</td>\n",
       "      <td>[4.75, 777.74, 1040.31, 1654.06, 1898.51]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>116184</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>fabaff</td>\n",
       "      <td>[276.82, 207, 1144.26, 1208.32, 1679.7]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>105994585</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>p-linnane</td>\n",
       "      <td>[0, 0, 0, 1105.82, 1300.46]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>195327</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>frenck</td>\n",
       "      <td>[389.28, 918.57, 1739.47, 1559.47, 1210.99]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>26833433</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>glenn-jocher</td>\n",
       "      <td>[114.57, 371.08, 723.53, 737.96, 1205.96]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>55035720</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>content-bot</td>\n",
       "      <td>[23.08, 120.54, 580.66, 675.92, 1107.72]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>56779163</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>SpecterShell</td>\n",
       "      <td>[0, 0, 63.65, 440.1, 1103.59]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>79238446</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>mssonicbld</td>\n",
       "      <td>[0, 0, 56.12, 151.2, 1073.56]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>28706372</td>\n",
       "      <td>GitHub</td>\n",
       "      <td>danielroe</td>\n",
       "      <td>[53.14, 127.23, 271.02, 674.22, 1034.67]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id platform          name  \\\n",
       "0    1580956   GitHub    chenrui333   \n",
       "1     663432   GitHub        bdraco   \n",
       "2     116184   GitHub        fabaff   \n",
       "3  105994585   GitHub     p-linnane   \n",
       "4     195327   GitHub        frenck   \n",
       "5   26833433   GitHub  glenn-jocher   \n",
       "6   55035720   GitHub   content-bot   \n",
       "7   56779163   GitHub  SpecterShell   \n",
       "8   79238446   GitHub    mssonicbld   \n",
       "9   28706372   GitHub     danielroe   \n",
       "\n",
       "                                      openrank  \n",
       "0      [674.78, 1261.4, 1533.26, 988, 2039.17]  \n",
       "1    [4.75, 777.74, 1040.31, 1654.06, 1898.51]  \n",
       "2      [276.82, 207, 1144.26, 1208.32, 1679.7]  \n",
       "3                  [0, 0, 0, 1105.82, 1300.46]  \n",
       "4  [389.28, 918.57, 1739.47, 1559.47, 1210.99]  \n",
       "5    [114.57, 371.08, 723.53, 737.96, 1205.96]  \n",
       "6     [23.08, 120.54, 580.66, 675.92, 1107.72]  \n",
       "7                [0, 0, 63.65, 440.1, 1103.59]  \n",
       "8                [0, 0, 56.12, 151.2, 1073.56]  \n",
       "9     [53.14, 127.23, 271.02, 674.22, 1034.67]  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_openrank = openDigger.index.openrank.getUserOpenrank(options)\n",
    "pd.DataFrame(list(data_user_openrank))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ca13223f-68a8-4f56-8bbb-46bb5f2996fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def row_openrank_postprocess(row):\n",
    "    years = [2019, 2020, 2021, 2022, 2023]\n",
    "    return {\n",
    "        \"x\": years,\n",
    "        \"y\": row[\"openrank\"],\n",
    "        \"name\": row[\"name\"]\n",
    "    }\n",
    "\n",
    "def processTechFieldRepoOpenrank(options, title):\n",
    "    data = openDigger.index.openrank.getRepoOpenrank(\n",
    "        dict(baseOptions, \n",
    "             **dict(options)\n",
    "            )\n",
    "    )\n",
    "    data_records = pd.DataFrame(data).apply(row_openrank_postprocess, axis=1).values.tolist()\n",
    "    openDigger.render.plotly(\n",
    "        data_records,\n",
    "        {\"title\": f\"Top 10 OpenRank repositories of {title} 2019-2023\"},\n",
    "        MultiMime()\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "35b071e2-f0f9-4e3c-9fb8-44e735b35e6b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "name": "elastic/kibana",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          5850.92,
          7653.21,
          7738.96,
          7880.61,
          7601.04
         ]
        },
        {
         "name": "grafana/grafana",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          3362.24,
          4710.99,
          5500.94,
          6195.07,
          7134.37
         ]
        },
        {
         "name": "ClickHouse/ClickHouse",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          2186.3,
          3327.7,
          4114.29,
          4345.95,
          4941.99
         ]
        },
        {
         "name": "airbytehq/airbyte",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          0,
          72.4,
          1625.84,
          4172.65,
          4658.86
         ]
        },
        {
         "name": "apache/doris",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          500.37,
          961.46,
          940.77,
          2296.17,
          4307.26
         ]
        },
        {
         "name": "elastic/elasticsearch",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          4503.12,
          4754.73,
          4229.9,
          3806.69,
          3729.39
         ]
        },
        {
         "name": "apache/airflow",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          1587.27,
          2775.71,
          3699.36,
          3550.43,
          3642.9
         ]
        },
        {
         "name": "StarRocks/starrocks",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          0,
          0,
          239.66,
          2220.96,
          3194.56
         ]
        },
        {
         "name": "trinodb/trino",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          951.83,
          1842.82,
          2247.44,
          2620.59,
          2703.4
         ]
        },
        {
         "name": "apache/spark",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          3050.64,
          3010.47,
          2618.08,
          2322,
          2654.02
         ]
        }
       ],
       "layout": {
        "autosize": true,
        "title": {
         "text": "Top 10 OpenRank repositories of Big Data 2019-2023"
        },
        "xaxis": {
         "autorange": true,
         "range": [
          2018.7546199805474,
          2023.2453800194526
         ],
         "type": "linear"
        },
        "yaxis": {
         "autorange": true,
         "range": [
          -650.3416019417476,
          8530.951601941748
         ],
         "type": "linear"
        }
       }
      },
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "processTechFieldRepoOpenrank({\"labelIntersect\": [':technology/big_data']}, 'Big Data');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "13ddee40-597a-4b4a-a9c4-81c1f1fc42fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def row_activity_postprocess(row):\n",
    "    years = [2019, 2020, 2021, 2022, 2023]\n",
    "    return {\n",
    "        \"x\": years,\n",
    "        \"y\": row[\"activity\"],\n",
    "        \"name\": row[\"name\"]\n",
    "    }\n",
    "def processTechFieldRepoActivity(options, title):\n",
    "    data = openDigger.index.activity.getRepoActivity(\n",
    "        dict(baseOptions, \n",
    "             **dict(options)\n",
    "            )\n",
    "    )\n",
    "    data_records = pd.DataFrame(data).apply(row_activity_postprocess, axis=1).values.tolist()\n",
    "    openDigger.render.plotly(\n",
    "        data_records, \n",
    "        {\"title\": f\"Top 10 Activity repositories of {title} 2019-2023\"},\n",
    "        MultiMime()\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c67752f-1eb7-4858-8bf8-0f9ceda56bfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "name": "elastic/kibana",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          19081,
          24101.53,
          22269.45,
          21462.77,
          20396.34
         ]
        },
        {
         "name": "grafana/grafana",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          9634.56,
          12828.86,
          13856.86,
          16400.21,
          18601.91
         ]
        },
        {
         "name": "ClickHouse/ClickHouse",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          5658.17,
          8471.87,
          10070.79,
          11201.19,
          12418.45
         ]
        },
        {
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         "y": [
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         "name": "StarRocks/starrocks",
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        {
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       ],
       "layout": {
        "autosize": true,
        "title": {
         "text": "Top 10 Activity repositories of Big Data 2019-2023"
        },
        "xaxis": {
         "autorange": true,
         "range": [
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          2023.2453800194526
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         "type": "linear"
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        "yaxis": {
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         "range": [
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         "type": "linear"
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "processTechFieldRepoActivity({\"labelIntersect\": [':technology/big_data']}, 'Big Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "63a4e8f1-c116-4ee3-a7fa-d315a6f1d747",
   "metadata": {},
   "outputs": [],
   "source": [
    "def activity_rank_postporcess(row):\n",
    "    return {\n",
    "        \"x\": [2019, 2020, 2021, 2022, 2023],\n",
    "        \"y\": row[\"values\"],\n",
    "        \"name\": row[\"name\"],\n",
    "        \"type\": 'scatter'\n",
    "    }\n",
    "def processRegionsCompanyRepoActivity(options, title):\n",
    "    data = openDigger.index.activity.getRepoActivity(dict({\n",
    "        \"startYear\": startYear, \"endYear\": endYear, \"startMonth\": startMonth, \"endMonth\": endMonth,\n",
    "        \"groupBy\": 'Company', \"groupTimeRange\": 'year', \"order\": 'DESC',\n",
    "    }, **options))\n",
    "    \n",
    "    data_records = pd.DataFrame(data).to_dict('records')\n",
    "    data_periodranks = openDigger.getRank(data_records, lambda x: x[\"name\"], lambda x: x[\"activity\"])\n",
    "    \n",
    "    data_periodranks_records = pd.DataFrame(data_periodranks).apply(activity_rank_postporcess, axis=1).values.tolist()\n",
    "    openDigger.render.plotly(data_periodranks_records, {\n",
    "        \"title\": f\"{title}活跃度 Top 10 {startYear} - {year}\", \n",
    "        \"xaxis\": { \"type\": 'category' }, \n",
    "        \"yaxis\": { \"autorange\": 'reversed', \"dtick\": 1 } \n",
    "    }, MultiMime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7cdd2827-e5ed-4bdd-90b9-32bdf6405818",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "name": "Others",
         "type": "scatter",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          1,
          1,
          1,
          1,
          1
         ]
        },
        {
         "name": "Microsoft",
         "type": "scatter",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          2,
          2,
          2,
          2,
          2
         ]
        },
        {
         "name": "Google",
         "type": "scatter",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          3,
          3,
          3,
          3,
          3
         ]
        },
        {
         "name": "Amazon",
         "type": "scatter",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          5,
          4,
          4,
          4,
          4
         ]
        },
        {
         "name": "Huawei",
         "type": "scatter",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          10,
          9,
          6,
          5,
          5
         ]
        },
        {
         "name": "Meta",
         "type": "scatter",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          4,
          5,
          5,
          6,
          6
         ]
        },
        {
         "name": "Alibaba",
         "type": "scatter",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          6,
          7,
          7,
          7,
          7
         ]
        },
        {
         "name": "HashiCorp",
         "type": "scatter",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          9,
          10,
          9,
          8,
          8
         ]
        },
        {
         "name": "Elastic",
         "type": "scatter",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          7,
          6,
          8,
          9,
          9
         ]
        },
        {
         "name": "Baidu",
         "type": "scatter",
         "x": [
          2019,
          2020,
          2021,
          2022,
          2023
         ],
         "y": [
          8,
          8,
          10,
          10,
          10
         ]
        }
       ],
       "layout": {
        "autosize": true,
        "title": {
         "text": "中国企业活跃度 Top 10 2019 - 2023"
        },
        "xaxis": {
         "autorange": true,
         "range": [
          -0.24346717654557043,
          4.24346717654557
         ],
         "type": "category"
        },
        "yaxis": {
         "autorange": true,
         "dtick": 1,
         "range": [
          10.742718446601941,
          0.25728155339805825
         ],
         "type": "linear"
        }
       }
      },
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "processRegionsCompanyRepoActivity({\"labelIntersect\": [':regions/CN', 'Company']}, \"中国企业\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2b9db08b-cca7-4e58-bcac-75e04f2b29b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<node_vm2.NodeVM at 0x7f5cf7c03430>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vmc.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbc750c6-388d-4784-9f6a-29cdd788e0bb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
